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In this paper adaptive control of a brushless DC motor (BLDCM) using neural network identification and pole shifting (PS) controller is presented. Proper system identification is one of the important factors that gives a good controller performance. This means that when the model parameter estimates are good, the controller output is good, whereas if the model parameter estimates are bad then almost surely the computed control will be bad. Proper selection of the identified system model order is also investigated. A comparison study between fuzzy logic controller and the proposed controller is also investigated.

This paper presents a laboratory set-up simulation of the wind energy power system including capacitor self-excited induction generator (CSEIG). An implementation of PI, PID and Fuzzy Logic (FLC) Controllers is developed. The hardware components and circuits are also designed and realized. The influence of these control techniques on the performance characteristics of the system under consideration is examined. The obtained experimental results show that the transient response and damping performance with FLC is better than that of PI and PD controllers.

This paper presents a study of the transient and dynamic stability enhancement and control of a synchronous generator connected to an infinite bus (single machine infinite bus system) via two parallel transmission lines when the power system working under different operating conditions and subjected to different disturbances. An effective mean of damping the oscillations resulting from these disturbances is to provide the synchronous generator with power system stabilizer. An adaptive fractional order proportional integral derivative controller is suggested to play the role of power system stabilizer in this paper. The results obtained from simulation study are presented and discussed.